Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations5605
Missing cells490
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory192.0 B

Variable types

DateTime3
Text3
Categorical10
Numeric8

Alerts

medicine_ended_or_recovered has constant value "1" Constant
age_category is highly overall correlated with age_of_patient and 2 other fieldsHigh correlation
age_of_patient is highly overall correlated with age_category and 2 other fieldsHigh correlation
base_cost is highly overall correlated with total_cost_of_medicineHigh correlation
code is highly overall correlated with age_category and 5 other fieldsHigh correlation
gender is highly overall correlated with code and 2 other fieldsHigh correlation
income is highly overall correlated with income_categoryHigh correlation
income_category is highly overall correlated with age_of_patient and 3 other fieldsHigh correlation
length_of_medication_in_days is highly overall correlated with medicine_dispensesHigh correlation
marital is highly overall correlated with age_category and 3 other fieldsHigh correlation
medicine_dispenses is highly overall correlated with length_of_medication_in_daysHigh correlation
payer_id is highly overall correlated with income_categoryHigh correlation
race is highly overall correlated with genderHigh correlation
reason_code_for_medication is highly overall correlated with code and 1 other fieldsHigh correlation
reason_description_for_medication is highly overall correlated with code and 1 other fieldsHigh correlation
total_cost_of_medicine is highly overall correlated with base_costHigh correlation
race is highly imbalanced (51.0%) Imbalance
ethnicity is highly imbalanced (73.8%) Imbalance
stop_time has 245 (4.4%) missing values Missing
length_of_medication_in_days has 245 (4.4%) missing values Missing
base_cost is highly skewed (γ1 = 43.181937) Skewed
payer_coverage is highly skewed (γ1 = 43.58464712) Skewed
total_cost_of_medicine is highly skewed (γ1 = 55.7427048) Skewed
length_of_medication_in_days is highly skewed (γ1 = 32.97911924) Skewed
payer_coverage has 2392 (42.7%) zeros Zeros
length_of_medication_in_days has 1081 (19.3%) zeros Zeros

Reproduction

Analysis started2024-12-02 13:24:33.222765
Analysis finished2024-12-02 13:24:43.175037
Duration9.95 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Distinct2615
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
Minimum1959-01-01 05:47:15
Maximum2024-11-01 02:26:29
2024-12-03T00:24:43.251355image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:43.369301image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

stop_time
Date

Missing 

Distinct2478
Distinct (%)46.2%
Missing245
Missing (%)4.4%
Memory size43.9 KiB
Minimum1963-06-13 13:37:48
Maximum2024-11-01 02:26:29
2024-12-03T00:24:43.486017image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:43.625071image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct105
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
2024-12-03T00:24:43.863082image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters201780
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row30a6452c-4297-a1ac-977a-6a23237c7b46
2nd row30a6452c-4297-a1ac-977a-6a23237c7b46
3rd row34a4dcc4-35fb-6ad5-ab98-be285c586a4f
4th row30a6452c-4297-a1ac-977a-6a23237c7b46
5th row34a4dcc4-35fb-6ad5-ab98-be285c586a4f
ValueCountFrequency (%)
bad5a231-3709-952a-cf44-f8d6a52cc214 1303
23.2%
d1622e8b-d26b-ec81-ffcb-ec4bf2af385b 702
12.5%
f3884e8a-8b36-1e93-66dd-e910dfab2ef5 616
11.0%
d27273f0-f62d-7d7f-746d-4565f35cf176 605
10.8%
cb1b46a1-9cb5-1187-ccc5-9fb7b98aa957 220
 
3.9%
00732e11-5e4d-37b7-01f8-929a25536862 170
 
3.0%
655baba7-47ed-22ac-2093-1196ebb44928 155
 
2.8%
916b1ac8-56c8-ec1b-3b9a-721336a74912 131
 
2.3%
bd2a8021-2868-6dd2-c17f-bfd7c36fe247 122
 
2.2%
14dc5e57-1b84-3305-c042-86c9fc7e4996 120
 
2.1%
Other values (95) 1461
26.1%
2024-12-03T00:24:44.209755image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 22420
 
11.1%
2 15825
 
7.8%
f 14156
 
7.0%
d 12104
 
6.0%
c 11846
 
5.9%
6 11807
 
5.9%
a 11651
 
5.8%
5 11531
 
5.7%
b 11530
 
5.7%
1 11354
 
5.6%
Other values (7) 67556
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 22420
 
11.1%
2 15825
 
7.8%
f 14156
 
7.0%
d 12104
 
6.0%
c 11846
 
5.9%
6 11807
 
5.9%
a 11651
 
5.8%
5 11531
 
5.7%
b 11530
 
5.7%
1 11354
 
5.6%
Other values (7) 67556
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 22420
 
11.1%
2 15825
 
7.8%
f 14156
 
7.0%
d 12104
 
6.0%
c 11846
 
5.9%
6 11807
 
5.9%
a 11651
 
5.8%
5 11531
 
5.7%
b 11530
 
5.7%
1 11354
 
5.6%
Other values (7) 67556
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 22420
 
11.1%
2 15825
 
7.8%
f 14156
 
7.0%
d 12104
 
6.0%
c 11846
 
5.9%
6 11807
 
5.9%
a 11651
 
5.8%
5 11531
 
5.7%
b 11530
 
5.7%
1 11354
 
5.6%
Other values (7) 67556
33.5%

payer_id
Categorical

High correlation 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
a735bf55-83e9-331a-899d-a82a60b9f60c
2881 
e03e23c9-4df1-3eb6-a62d-f70f02301496
1244 
df166300-5a78-3502-a46a-832842197811
486 
b046940f-1664-3047-bca7-dfa76be352a4
 
256
734afbd6-4794-363b-9bc0-6a3981533ed5
 
225
Other values (5)
513 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters201780
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd31fccc3-1767-390d-966a-22a5156f4219
2nd rowd31fccc3-1767-390d-966a-22a5156f4219
3rd rowe03e23c9-4df1-3eb6-a62d-f70f02301496
4th rowd31fccc3-1767-390d-966a-22a5156f4219
5th row8fa6c185-e44e-3e34-8bd8-39be8694f4ce

Common Values

ValueCountFrequency (%)
a735bf55-83e9-331a-899d-a82a60b9f60c 2881
51.4%
e03e23c9-4df1-3eb6-a62d-f70f02301496 1244
22.2%
df166300-5a78-3502-a46a-832842197811 486
 
8.7%
b046940f-1664-3047-bca7-dfa76be352a4 256
 
4.6%
734afbd6-4794-363b-9bc0-6a3981533ed5 225
 
4.0%
0133f751-9229-3cfd-815f-b6d4979bdd6a 174
 
3.1%
26aab0cd-6aba-3e1b-ac5b-05c8867e762c 156
 
2.8%
d31fccc3-1767-390d-966a-22a5156f4219 109
 
1.9%
8fa6c185-e44e-3e34-8bd8-39be8694f4ce 47
 
0.8%
d18ef2e6-ef40-324c-be54-34a5ee865625 27
 
0.5%

Length

2024-12-03T00:24:44.325308image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T00:24:44.445587image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
a735bf55-83e9-331a-899d-a82a60b9f60c 2881
51.4%
e03e23c9-4df1-3eb6-a62d-f70f02301496 1244
22.2%
df166300-5a78-3502-a46a-832842197811 486
 
8.7%
b046940f-1664-3047-bca7-dfa76be352a4 256
 
4.6%
734afbd6-4794-363b-9bc0-6a3981533ed5 225
 
4.0%
0133f751-9229-3cfd-815f-b6d4979bdd6a 174
 
3.1%
26aab0cd-6aba-3e1b-ac5b-05c8867e762c 156
 
2.8%
d31fccc3-1767-390d-966a-22a5156f4219 109
 
1.9%
8fa6c185-e44e-3e34-8bd8-39be8694f4ce 47
 
0.8%
d18ef2e6-ef40-324c-be54-34a5ee865625 27
 
0.5%

Most occurring characters

ValueCountFrequency (%)
- 22420
11.1%
3 21020
 
10.4%
a 16690
 
8.3%
9 16546
 
8.2%
6 14234
 
7.1%
0 13811
 
6.8%
f 11605
 
5.8%
8 11587
 
5.7%
5 11354
 
5.6%
b 9542
 
4.7%
Other values (7) 52971
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 22420
11.1%
3 21020
 
10.4%
a 16690
 
8.3%
9 16546
 
8.2%
6 14234
 
7.1%
0 13811
 
6.8%
f 11605
 
5.8%
8 11587
 
5.7%
5 11354
 
5.6%
b 9542
 
4.7%
Other values (7) 52971
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 22420
11.1%
3 21020
 
10.4%
a 16690
 
8.3%
9 16546
 
8.2%
6 14234
 
7.1%
0 13811
 
6.8%
f 11605
 
5.8%
8 11587
 
5.7%
5 11354
 
5.6%
b 9542
 
4.7%
Other values (7) 52971
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 22420
11.1%
3 21020
 
10.4%
a 16690
 
8.3%
9 16546
 
8.2%
6 14234
 
7.1%
0 13811
 
6.8%
f 11605
 
5.8%
8 11587
 
5.7%
5 11354
 
5.6%
b 9542
 
4.7%
Other values (7) 52971
26.3%
Distinct2578
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
2024-12-03T00:24:44.670738image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters201780
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1597 ?
Unique (%)28.5%

Sample

1st row953c5138-ce17-4084-3432-1ac23f184528
2nd row953c5138-ce17-4084-3432-1ac23f184528
3rd rowd1cea2e5-1735-089f-c72f-22ad16976663
4th row794baa15-fe5e-c061-e188-ad59022aeea5
5th row8a84efee-6fd7-f5b3-8816-9a1c60e720be
ValueCountFrequency (%)
d6a83835-7bbf-5aed-ceb1-1039fa5417e3 28
 
0.5%
275edbad-1214-e1d5-1bbf-5fc04d2e68b0 9
 
0.2%
bb908ad6-5d04-34c1-818b-cb7221f51f46 9
 
0.2%
ead70e1d-2b64-fc7d-d047-3c5d811c993b 9
 
0.2%
631b2013-81d5-f250-6d06-dabe6fc95630 9
 
0.2%
d43fa1a9-7705-ab8b-374a-ddc44221864b 9
 
0.2%
ddc13a1d-d1c6-308b-f637-9f7915381f0d 9
 
0.2%
5ed28b7a-d1e9-8d89-4ea7-17dc975f95ee 9
 
0.2%
e1dc8109-e21e-cac9-abb8-cc4979473ca5 9
 
0.2%
ccf0b32b-e70a-0690-56a6-027189b0b7c5 9
 
0.2%
Other values (2568) 5496
98.1%
2024-12-03T00:24:45.010056image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 22420
 
11.1%
b 11655
 
5.8%
a 11433
 
5.7%
7 11428
 
5.7%
8 11384
 
5.6%
c 11375
 
5.6%
6 11268
 
5.6%
1 11217
 
5.6%
0 11213
 
5.6%
d 11199
 
5.6%
Other values (7) 77188
38.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 22420
 
11.1%
b 11655
 
5.8%
a 11433
 
5.7%
7 11428
 
5.7%
8 11384
 
5.6%
c 11375
 
5.6%
6 11268
 
5.6%
1 11217
 
5.6%
0 11213
 
5.6%
d 11199
 
5.6%
Other values (7) 77188
38.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 22420
 
11.1%
b 11655
 
5.8%
a 11433
 
5.7%
7 11428
 
5.7%
8 11384
 
5.6%
c 11375
 
5.6%
6 11268
 
5.6%
1 11217
 
5.6%
0 11213
 
5.6%
d 11199
 
5.6%
Other values (7) 77188
38.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 22420
 
11.1%
b 11655
 
5.8%
a 11433
 
5.7%
7 11428
 
5.7%
8 11384
 
5.6%
c 11375
 
5.6%
6 11268
 
5.6%
1 11217
 
5.6%
0 11213
 
5.6%
d 11199
 
5.6%
Other values (7) 77188
38.3%

code
Real number (ℝ)

High correlation 

Distinct116
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean518218.68
Minimum106258
Maximum2119714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.9 KiB
2024-12-03T00:24:45.143793image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum106258
5-th percentile106892
Q1209387
median310798
Q3834061
95-th percentile1535362
Maximum2119714
Range2013456
Interquartile range (IQR)624674

Descriptive statistics

Standard deviation450113.87
Coefficient of variation (CV)0.86857901
Kurtosis1.0112971
Mean518218.68
Median Absolute Deviation (MAD)104875
Skewness1.4383095
Sum2.9046157 × 109
Variance2.0260249 × 1011
MonotonicityNot monotonic
2024-12-03T00:24:45.408518image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
314076 747
13.3%
205923 645
 
11.5%
106892 499
 
8.9%
310798 488
 
8.7%
308136 448
 
8.0%
1535362 311
 
5.5%
209387 301
 
5.4%
1664463 181
 
3.2%
245314 174
 
3.1%
835603 154
 
2.7%
Other values (106) 1657
29.6%
ValueCountFrequency (%)
106258 2
 
< 0.1%
106892 499
8.9%
108515 37
 
0.7%
197319 1
 
< 0.1%
197454 5
 
0.1%
197511 2
 
< 0.1%
197591 2
 
< 0.1%
198014 2
 
< 0.1%
198240 2
 
< 0.1%
198335 2
 
< 0.1%
ValueCountFrequency (%)
2119714 3
 
0.1%
2001499 4
 
0.1%
1873983 1
 
< 0.1%
1870230 10
0.2%
1860491 7
0.1%
1860480 2
 
< 0.1%
1860154 16
0.3%
1856546 1
 
< 0.1%
1808217 1
 
< 0.1%
1807510 1
 
< 0.1%
Distinct121
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
2024-12-03T00:24:45.642177image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length113
Median length92
Mean length47.213024
Min length15

Characters and Unicode

Total characters264629
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)0.5%

Sample

1st rowAcetaminophen 325 MG / HYDROcodone Bitartrate 7.5 MG Oral Tablet
2nd rowIbuprofen 200 MG Oral Tablet
3rd rowferrous sulfate 325 MG Oral Tablet
4th rowAcetaminophen 325 MG Oral Tablet
5th rowsodium fluoride 0.0272 MG/MG Oral Gel
ValueCountFrequency (%)
mg 3532
 
8.8%
oral 3512
 
8.7%
tablet 3185
 
7.9%
unt/ml 1647
 
4.1%
insulin 998
 
2.5%
human 998
 
2.5%
10 944
 
2.3%
1 903
 
2.2%
848
 
2.1%
lisinopril 747
 
1.9%
Other values (230) 22946
57.0%
2024-12-03T00:24:46.036097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36163
 
13.7%
e 17173
 
6.5%
l 16616
 
6.3%
a 15195
 
5.7%
n 14811
 
5.6%
i 12985
 
4.9%
o 11983
 
4.5%
r 10137
 
3.8%
t 9691
 
3.7%
M 8267
 
3.1%
Other values (56) 111608
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 264629
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
36163
 
13.7%
e 17173
 
6.5%
l 16616
 
6.3%
a 15195
 
5.7%
n 14811
 
5.6%
i 12985
 
4.9%
o 11983
 
4.5%
r 10137
 
3.8%
t 9691
 
3.7%
M 8267
 
3.1%
Other values (56) 111608
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 264629
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
36163
 
13.7%
e 17173
 
6.5%
l 16616
 
6.3%
a 15195
 
5.7%
n 14811
 
5.6%
i 12985
 
4.9%
o 11983
 
4.5%
r 10137
 
3.8%
t 9691
 
3.7%
M 8267
 
3.1%
Other values (56) 111608
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 264629
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
36163
 
13.7%
e 17173
 
6.5%
l 16616
 
6.3%
a 15195
 
5.7%
n 14811
 
5.6%
i 12985
 
4.9%
o 11983
 
4.5%
r 10137
 
3.8%
t 9691
 
3.7%
M 8267
 
3.1%
Other values (56) 111608
42.2%

base_cost
Real number (ℝ)

High correlation  Skewed 

Distinct845
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.60708
Minimum0.01
Maximum103958.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.9 KiB
2024-12-03T00:24:46.181996image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.45
Q10.91
median40.79
Q3129.94
95-th percentile296.37
Maximum103958.4
Range103958.39
Interquartile range (IQR)129.03

Descriptive statistics

Standard deviation2316.235
Coefficient of variation (CV)16.128975
Kurtosis1875.4342
Mean143.60708
Median Absolute Deviation (MAD)40.34
Skewness43.181937
Sum804917.69
Variance5364944.5
MonotonicityNot monotonic
2024-12-03T00:24:46.321651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.94 1880
33.5%
0.91 1108
19.8%
29.21 409
 
7.3%
0.45 318
 
5.7%
30.67 220
 
3.9%
126.89 185
 
3.3%
1.37 97
 
1.7%
507.86 75
 
1.3%
45.86 47
 
0.8%
6.87 44
 
0.8%
Other values (835) 1222
21.8%
ValueCountFrequency (%)
0.01 8
0.1%
0.02 1
 
< 0.1%
0.03 5
0.1%
0.09 1
 
< 0.1%
0.11 1
 
< 0.1%
0.13 1
 
< 0.1%
0.15 5
0.1%
0.18 1
 
< 0.1%
0.22 1
 
< 0.1%
0.27 1
 
< 0.1%
ValueCountFrequency (%)
103958.4 2
< 0.1%
91728 1
< 0.1%
4517.84 1
< 0.1%
2421.72 1
< 0.1%
1886.37 1
< 0.1%
1507.01 1
< 0.1%
1347.43 1
< 0.1%
994.39 1
< 0.1%
944.05 1
< 0.1%
917.28 2
< 0.1%

payer_coverage
Real number (ℝ)

Skewed  Zeros 

Distinct553
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.287058
Minimum0
Maximum91678
Zeros2392
Zeros (%)42.7%
Negative0
Negative (%)0.0%
Memory size43.9 KiB
2024-12-03T00:24:46.456740image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.73
Q3101.51
95-th percentile167.172
Maximum91678
Range91678
Interquartile range (IQR)101.51

Descriptive statistics

Standard deviation1919.087
Coefficient of variation (CV)21.493451
Kurtosis1918.6258
Mean89.287058
Median Absolute Deviation (MAD)0.73
Skewness43.584647
Sum500453.96
Variance3682895.1
MonotonicityNot monotonic
2024-12-03T00:24:46.584888image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2392
42.7%
103.95 896
 
16.0%
0.73 531
 
9.5%
23.37 305
 
5.4%
24.54 163
 
2.9%
0.36 127
 
2.3%
101.51 119
 
2.1%
79.94 99
 
1.8%
0.91 60
 
1.1%
129.94 58
 
1.0%
Other values (543) 855
 
15.3%
ValueCountFrequency (%)
0 2392
42.7%
0.07 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 2
 
< 0.1%
0.14 1
 
< 0.1%
0.18 2
 
< 0.1%
0.24 8
 
0.1%
0.25 1
 
< 0.1%
0.31 1
 
< 0.1%
0.32 1
 
< 0.1%
ValueCountFrequency (%)
91678 1
< 0.1%
83166.72 1
< 0.1%
72770.88 1
< 0.1%
3614.27 1
< 0.1%
1322.7 1
< 0.1%
1312.43 1
< 0.1%
828.12 1
< 0.1%
764.4 2
< 0.1%
733.82 2
< 0.1%
714.4 1
< 0.1%

medicine_dispenses
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2453167
Minimum1
Maximum801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.9 KiB
2024-12-03T00:24:46.699458image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile12
Maximum801
Range800
Interquartile range (IQR)2

Descriptive statistics

Standard deviation40.035579
Coefficient of variation (CV)6.4104962
Kurtosis174.92002
Mean6.2453167
Median Absolute Deviation (MAD)0
Skewness12.26836
Sum35005
Variance1602.8476
MonotonicityNot monotonic
2024-12-03T00:24:46.834276image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3894
69.5%
3 942
 
16.8%
4 272
 
4.9%
12 217
 
3.9%
2 116
 
2.1%
10 19
 
0.3%
7 12
 
0.2%
6 12
 
0.2%
9 8
 
0.1%
5 6
 
0.1%
Other values (52) 107
 
1.9%
ValueCountFrequency (%)
1 3894
69.5%
2 116
 
2.1%
3 942
 
16.8%
4 272
 
4.9%
5 6
 
0.1%
6 12
 
0.2%
7 12
 
0.2%
8 6
 
0.1%
9 8
 
0.1%
10 19
 
0.3%
ValueCountFrequency (%)
801 1
< 0.1%
751 1
< 0.1%
730 1
< 0.1%
718 1
< 0.1%
694 1
< 0.1%
638 1
< 0.1%
631 1
< 0.1%
553 2
< 0.1%
525 1
< 0.1%
496 1
< 0.1%

total_cost_of_medicine
Real number (ℝ)

High correlation  Skewed 

Distinct977
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7807.0456
Minimum0.01
Maximum23182723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.9 KiB
2024-12-03T00:24:46.961806image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.58
Q11.37
median67.73
Q3233.13
95-th percentile733.638
Maximum23182723
Range23182723
Interquartile range (IQR)231.76

Descriptive statistics

Standard deviation372640.65
Coefficient of variation (CV)47.731327
Kurtosis3201.5132
Mean7807.0456
Median Absolute Deviation (MAD)66.81
Skewness55.742705
Sum43758491
Variance1.3886106 × 1011
MonotonicityNot monotonic
2024-12-03T00:24:47.097667image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.94 1012
18.1%
0.91 907
16.2%
389.82 835
14.9%
29.21 409
 
7.3%
0.45 229
 
4.1%
30.67 220
 
3.9%
126.89 141
 
2.5%
3.64 135
 
2.4%
1.37 77
 
1.4%
507.86 61
 
1.1%
Other values (967) 1579
28.2%
ValueCountFrequency (%)
0.01 8
 
0.1%
0.02 1
 
< 0.1%
0.03 5
 
0.1%
0.09 1
 
< 0.1%
0.11 1
 
< 0.1%
0.13 1
 
< 0.1%
0.15 1
 
< 0.1%
0.18 1
 
< 0.1%
0.22 1
 
< 0.1%
0.45 229
4.1%
ValueCountFrequency (%)
23182723.2 1
< 0.1%
15489801.6 1
< 0.1%
967454.74 1
< 0.1%
290361.88 1
< 0.1%
101092.96 1
< 0.1%
96965.9 1
< 0.1%
91728 1
< 0.1%
89604.9 1
< 0.1%
87025.98 1
< 0.1%
82113.84 1
< 0.1%

reason_code_for_medication
Categorical

High correlation 

Distinct37
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
59621000.0
1683 
Unknown
1271 
271737000.0
645 
233678006.0
628 
44054006.0
267 
Other values (32)
1111 

Length

Max length11
Median length10
Mean length9.6754683
Min length7

Characters and Unicode

Total characters54231
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.2%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th row66383009.0

Common Values

ValueCountFrequency (%)
59621000.0 1683
30.0%
Unknown 1271
22.7%
271737000.0 645
 
11.5%
233678006.0 628
 
11.2%
44054006.0 267
 
4.8%
714628002.0 245
 
4.4%
161665007.0 214
 
3.8%
103697008.0 161
 
2.9%
66383009.0 148
 
2.6%
64859006.0 74
 
1.3%
Other values (27) 269
 
4.8%

Length

2024-12-03T00:24:47.231145image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
59621000.0 1683
30.0%
unknown 1271
22.7%
271737000.0 645
 
11.5%
233678006.0 628
 
11.2%
44054006.0 267
 
4.8%
714628002.0 245
 
4.4%
161665007.0 214
 
3.8%
103697008.0 161
 
2.9%
66383009.0 148
 
2.6%
64859006.0 74
 
1.3%
Other values (27) 269
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 15944
29.4%
6 4781
 
8.8%
. 4334
 
8.0%
n 3813
 
7.0%
2 3675
 
6.8%
1 3307
 
6.1%
7 3294
 
6.1%
3 2498
 
4.6%
5 2451
 
4.5%
9 2211
 
4.1%
Other values (6) 7923
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15944
29.4%
6 4781
 
8.8%
. 4334
 
8.0%
n 3813
 
7.0%
2 3675
 
6.8%
1 3307
 
6.1%
7 3294
 
6.1%
3 2498
 
4.6%
5 2451
 
4.5%
9 2211
 
4.1%
Other values (6) 7923
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15944
29.4%
6 4781
 
8.8%
. 4334
 
8.0%
n 3813
 
7.0%
2 3675
 
6.8%
1 3307
 
6.1%
7 3294
 
6.1%
3 2498
 
4.6%
5 2451
 
4.5%
9 2211
 
4.1%
Other values (6) 7923
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15944
29.4%
6 4781
 
8.8%
. 4334
 
8.0%
n 3813
 
7.0%
2 3675
 
6.8%
1 3307
 
6.1%
7 3294
 
6.1%
3 2498
 
4.6%
5 2451
 
4.5%
9 2211
 
4.1%
Other values (6) 7923
14.6%

reason_description_for_medication
Categorical

High correlation 

Distinct37
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
Essential hypertension (disorder)
1683 
Unknown
1271 
Anemia (disorder)
645 
Childhood asthma (disorder)
628 
Diabetes mellitus type 2 (disorder)
267 
Other values (32)
1111 

Length

Max length67
Median length59
Mean length24.196967
Min length7

Characters and Unicode

Total characters135624
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.2%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowGingivitis (disorder)

Common Values

ValueCountFrequency (%)
Essential hypertension (disorder) 1683
30.0%
Unknown 1271
22.7%
Anemia (disorder) 645
 
11.5%
Childhood asthma (disorder) 628
 
11.2%
Diabetes mellitus type 2 (disorder) 267
 
4.8%
Prediabetes (finding) 245
 
4.4%
History of renal transplant (situation) 214
 
3.8%
Patient referral for dental care (procedure) 161
 
2.9%
Gingivitis (disorder) 148
 
2.6%
Osteoporosis (disorder) 74
 
1.3%
Other values (27) 269
 
4.8%

Length

2024-12-03T00:24:47.368671image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
disorder 3717
25.3%
essential 1683
11.4%
hypertension 1683
11.4%
unknown 1271
 
8.6%
anemia 645
 
4.4%
childhood 628
 
4.3%
asthma 628
 
4.3%
mellitus 267
 
1.8%
diabetes 267
 
1.8%
type 267
 
1.8%
Other values (74) 3646
24.8%

Most occurring characters

ValueCountFrequency (%)
e 13079
 
9.6%
i 11722
 
8.6%
n 11589
 
8.5%
r 11586
 
8.5%
s 11514
 
8.5%
d 9584
 
7.1%
o 9470
 
7.0%
9100
 
6.7%
t 7301
 
5.4%
a 5901
 
4.4%
Other values (35) 34778
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13079
 
9.6%
i 11722
 
8.6%
n 11589
 
8.5%
r 11586
 
8.5%
s 11514
 
8.5%
d 9584
 
7.1%
o 9470
 
7.0%
9100
 
6.7%
t 7301
 
5.4%
a 5901
 
4.4%
Other values (35) 34778
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13079
 
9.6%
i 11722
 
8.6%
n 11589
 
8.5%
r 11586
 
8.5%
s 11514
 
8.5%
d 9584
 
7.1%
o 9470
 
7.0%
9100
 
6.7%
t 7301
 
5.4%
a 5901
 
4.4%
Other values (35) 34778
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13079
 
9.6%
i 11722
 
8.6%
n 11589
 
8.5%
r 11586
 
8.5%
s 11514
 
8.5%
d 9584
 
7.1%
o 9470
 
7.0%
9100
 
6.7%
t 7301
 
5.4%
a 5901
 
4.4%
Other values (35) 34778
25.6%

length_of_medication_in_days
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct134
Distinct (%)2.5%
Missing245
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean0.20809037
Minimum0
Maximum38.471233
Zeros1081
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size43.9 KiB
2024-12-03T00:24:47.491987image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.019178082
median0.057534247
Q30.13424658
95-th percentile1.0164384
Maximum38.471233
Range38.471233
Interquartile range (IQR)0.11506849

Descriptive statistics

Standard deviation0.76832683
Coefficient of variation (CV)3.6922748
Kurtosis1441.3973
Mean0.20809037
Median Absolute Deviation (MAD)0.038356164
Skewness32.979119
Sum1115.3644
Variance0.59032612
MonotonicityNot monotonic
2024-12-03T00:24:47.641852image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01917808219 1137
20.3%
0 1081
19.3%
0.07671232877 712
12.7%
1.016438356 452
 
8.1%
0.05753424658 359
 
6.4%
0.09589041096 269
 
4.8%
0.03835616438 151
 
2.7%
0.1534246575 143
 
2.6%
0.1150684932 116
 
2.1%
0.1726027397 99
 
1.8%
Other values (124) 841
15.0%
(Missing) 245
 
4.4%
ValueCountFrequency (%)
0 1081
19.3%
0.002739726027 10
 
0.2%
0.01917808219 1137
20.3%
0.02191780822 10
 
0.2%
0.02465753425 2
 
< 0.1%
0.02739726027 6
 
0.1%
0.0301369863 9
 
0.2%
0.03287671233 11
 
0.2%
0.03561643836 7
 
0.1%
0.03835616438 151
 
2.7%
ValueCountFrequency (%)
38.47123288 1
< 0.1%
26.40821918 1
< 0.1%
15.07671233 1
< 0.1%
13.26849315 1
< 0.1%
6.060273973 1
< 0.1%
4.161643836 1
< 0.1%
3.032876712 1
< 0.1%
2.032876712 1
< 0.1%
1.120547945 1
< 0.1%
1.068493151 1
< 0.1%

medicine_ended_or_recovered
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
1
5605 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5605
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5605
100.0%

Length

2024-12-03T00:24:47.761558image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T00:24:47.840049image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 5605
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5605
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5605
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5605
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5605
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5605
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5605
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5605
100.0%
Distinct99
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
Minimum1914-03-03 00:00:00
Maximum2023-03-01 00:00:00
2024-12-03T00:24:47.931410image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:48.064580image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

marital
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
M
3636 
D
1001 
Unknown
399 
S
 
333
W
 
236

Length

Max length7
Median length1
Mean length1.4271186
Min length1

Characters and Unicode

Total characters7999
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowD
4th rowM
5th rowD

Common Values

ValueCountFrequency (%)
M 3636
64.9%
D 1001
 
17.9%
Unknown 399
 
7.1%
S 333
 
5.9%
W 236
 
4.2%

Length

2024-12-03T00:24:48.188146image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T00:24:48.292833image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
m 3636
64.9%
d 1001
 
17.9%
unknown 399
 
7.1%
s 333
 
5.9%
w 236
 
4.2%

Most occurring characters

ValueCountFrequency (%)
M 3636
45.5%
n 1197
 
15.0%
D 1001
 
12.5%
U 399
 
5.0%
k 399
 
5.0%
o 399
 
5.0%
w 399
 
5.0%
S 333
 
4.2%
W 236
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 3636
45.5%
n 1197
 
15.0%
D 1001
 
12.5%
U 399
 
5.0%
k 399
 
5.0%
o 399
 
5.0%
w 399
 
5.0%
S 333
 
4.2%
W 236
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 3636
45.5%
n 1197
 
15.0%
D 1001
 
12.5%
U 399
 
5.0%
k 399
 
5.0%
o 399
 
5.0%
w 399
 
5.0%
S 333
 
4.2%
W 236
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 3636
45.5%
n 1197
 
15.0%
D 1001
 
12.5%
U 399
 
5.0%
k 399
 
5.0%
o 399
 
5.0%
w 399
 
5.0%
S 333
 
4.2%
W 236
 
3.0%

race
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
white
4194 
asian
935 
other
 
281
black
 
187
native
 
8

Length

Max length6
Median length5
Mean length5.0014273
Min length5

Characters and Unicode

Total characters28033
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowwhite
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 4194
74.8%
asian 935
 
16.7%
other 281
 
5.0%
black 187
 
3.3%
native 8
 
0.1%

Length

2024-12-03T00:24:48.396115image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T00:24:48.486876image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
white 4194
74.8%
asian 935
 
16.7%
other 281
 
5.0%
black 187
 
3.3%
native 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 5137
18.3%
e 4483
16.0%
t 4483
16.0%
h 4475
16.0%
w 4194
15.0%
a 2065
7.4%
n 943
 
3.4%
s 935
 
3.3%
o 281
 
1.0%
r 281
 
1.0%
Other values (5) 756
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 5137
18.3%
e 4483
16.0%
t 4483
16.0%
h 4475
16.0%
w 4194
15.0%
a 2065
7.4%
n 943
 
3.4%
s 935
 
3.3%
o 281
 
1.0%
r 281
 
1.0%
Other values (5) 756
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 5137
18.3%
e 4483
16.0%
t 4483
16.0%
h 4475
16.0%
w 4194
15.0%
a 2065
7.4%
n 943
 
3.4%
s 935
 
3.3%
o 281
 
1.0%
r 281
 
1.0%
Other values (5) 756
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 5137
18.3%
e 4483
16.0%
t 4483
16.0%
h 4475
16.0%
w 4194
15.0%
a 2065
7.4%
n 943
 
3.4%
s 935
 
3.3%
o 281
 
1.0%
r 281
 
1.0%
Other values (5) 756
 
2.7%

ethnicity
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
nonhispanic
5356 
hispanic
 
249

Length

Max length11
Median length11
Mean length10.866726
Min length8

Characters and Unicode

Total characters60908
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonhispanic
2nd rownonhispanic
3rd rownonhispanic
4th rownonhispanic
5th rownonhispanic

Common Values

ValueCountFrequency (%)
nonhispanic 5356
95.6%
hispanic 249
 
4.4%

Length

2024-12-03T00:24:48.598039image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T00:24:48.689724image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
nonhispanic 5356
95.6%
hispanic 249
 
4.4%

Most occurring characters

ValueCountFrequency (%)
n 16317
26.8%
i 11210
18.4%
h 5605
 
9.2%
s 5605
 
9.2%
a 5605
 
9.2%
p 5605
 
9.2%
c 5605
 
9.2%
o 5356
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60908
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 16317
26.8%
i 11210
18.4%
h 5605
 
9.2%
s 5605
 
9.2%
a 5605
 
9.2%
p 5605
 
9.2%
c 5605
 
9.2%
o 5356
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60908
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 16317
26.8%
i 11210
18.4%
h 5605
 
9.2%
s 5605
 
9.2%
a 5605
 
9.2%
p 5605
 
9.2%
c 5605
 
9.2%
o 5356
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60908
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 16317
26.8%
i 11210
18.4%
h 5605
 
9.2%
s 5605
 
9.2%
a 5605
 
9.2%
p 5605
 
9.2%
c 5605
 
9.2%
o 5356
 
8.8%

gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
M
3702 
F
1903 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5605
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 3702
66.0%
F 1903
34.0%

Length

2024-12-03T00:24:48.773642image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T00:24:48.853477image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
m 3702
66.0%
f 1903
34.0%

Most occurring characters

ValueCountFrequency (%)
M 3702
66.0%
F 1903
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5605
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 3702
66.0%
F 1903
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5605
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 3702
66.0%
F 1903
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5605
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 3702
66.0%
F 1903
34.0%

income
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95801.487
Minimum7361
Maximum816851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.9 KiB
2024-12-03T00:24:48.959281image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum7361
5-th percentile26923
Q158212
median90297
Q3118047
95-th percentile179090
Maximum816851
Range809490
Interquartile range (IQR)59835

Descriptive statistics

Standard deviation91183.612
Coefficient of variation (CV)0.95179746
Kurtosis43.416419
Mean95801.487
Median Absolute Deviation (MAD)27750
Skewness6.0009987
Sum5.3696734 × 108
Variance8.3144512 × 109
MonotonicityNot monotonic
2024-12-03T00:24:49.230468image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118047 1303
23.2%
92537 727
13.0%
35860 616
11.0%
82922 605
10.8%
90297 277
 
4.9%
63727 220
 
3.9%
30550 170
 
3.0%
140772 131
 
2.3%
72413 120
 
2.1%
189277 101
 
1.8%
Other values (89) 1335
23.8%
ValueCountFrequency (%)
7361 31
0.6%
7873 2
 
< 0.1%
8615 4
 
0.1%
8752 10
 
0.2%
10135 5
 
0.1%
10682 24
0.4%
12128 14
0.2%
16969 20
0.4%
17382 30
0.5%
18258 5
 
0.1%
ValueCountFrequency (%)
816851 53
0.9%
762068 7
 
0.1%
742063 10
 
0.2%
550030 11
 
0.2%
198522 12
 
0.2%
198442 27
 
0.5%
189277 101
1.8%
188023 8
 
0.1%
179090 83
1.5%
178323 7
 
0.1%

income_category
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
high-income
3008 
low-income
1359 
medium-income
1238 

Length

Max length13
Median length11
Mean length11.199286
Min length10

Characters and Unicode

Total characters62772
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh-income
2nd rowhigh-income
3rd rowmedium-income
4th rowhigh-income
5th rowmedium-income

Common Values

ValueCountFrequency (%)
high-income 3008
53.7%
low-income 1359
24.2%
medium-income 1238
22.1%

Length

2024-12-03T00:24:49.366501image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T00:24:49.481676image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
high-income 3008
53.7%
low-income 1359
24.2%
medium-income 1238
22.1%

Most occurring characters

ValueCountFrequency (%)
i 9851
15.7%
m 8081
12.9%
o 6964
11.1%
e 6843
10.9%
h 6016
9.6%
- 5605
8.9%
n 5605
8.9%
c 5605
8.9%
g 3008
 
4.8%
l 1359
 
2.2%
Other values (3) 3835
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 9851
15.7%
m 8081
12.9%
o 6964
11.1%
e 6843
10.9%
h 6016
9.6%
- 5605
8.9%
n 5605
8.9%
c 5605
8.9%
g 3008
 
4.8%
l 1359
 
2.2%
Other values (3) 3835
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 9851
15.7%
m 8081
12.9%
o 6964
11.1%
e 6843
10.9%
h 6016
9.6%
- 5605
8.9%
n 5605
8.9%
c 5605
8.9%
g 3008
 
4.8%
l 1359
 
2.2%
Other values (3) 3835
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 9851
15.7%
m 8081
12.9%
o 6964
11.1%
e 6843
10.9%
h 6016
9.6%
- 5605
8.9%
n 5605
8.9%
c 5605
8.9%
g 3008
 
4.8%
l 1359
 
2.2%
Other values (3) 3835
 
6.1%

age_of_patient
Real number (ℝ)

High correlation 

Distinct2204
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.670392
Minimum0.10136986
Maximum110.04384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.9 KiB
2024-12-03T00:24:49.605093image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.10136986
5-th percentile18.22137
Q142.575342
median58.454795
Q364.939726
95-th percentile78.281096
Maximum110.04384
Range109.94247
Interquartile range (IQR)22.364384

Descriptive statistics

Standard deviation18.145749
Coefficient of variation (CV)0.33191182
Kurtosis0.71778371
Mean54.670392
Median Absolute Deviation (MAD)11.065753
Skewness-0.5743194
Sum306427.55
Variance329.26821
MonotonicityNot monotonic
2024-12-03T00:24:49.734673image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.67671233 14
 
0.2%
61.23561644 14
 
0.2%
62.88493151 13
 
0.2%
59.83561644 13
 
0.2%
59.18356164 13
 
0.2%
56.13424658 12
 
0.2%
62.4630137 12
 
0.2%
61.8109589 11
 
0.2%
57.49589041 11
 
0.2%
61.75342466 11
 
0.2%
Other values (2194) 5481
97.8%
ValueCountFrequency (%)
0.101369863 2
< 0.1%
0.1150684932 1
 
< 0.1%
0.2465753425 2
< 0.1%
0.4602739726 1
 
< 0.1%
0.4931506849 2
< 0.1%
0.7397260274 4
0.1%
0.7534246575 1
 
< 0.1%
0.7780821918 2
< 0.1%
0.8520547945 1
 
< 0.1%
0.8849315068 1
 
< 0.1%
ValueCountFrequency (%)
110.0438356 1
< 0.1%
110.0054795 2
< 0.1%
108.9890411 2
< 0.1%
108.0109589 1
< 0.1%
107.9726027 2
< 0.1%
107.6931507 1
< 0.1%
107.339726 2
< 0.1%
107.309589 1
< 0.1%
106.9561644 2
< 0.1%
106.4164384 2
< 0.1%

age_category
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
adult
3958 
senior
1395 
children
 
252

Length

Max length8
Median length5
Mean length5.3837645
Min length5

Characters and Unicode

Total characters30176
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadult
2nd rowadult
3rd rowadult
4th rowadult
5th rowadult

Common Values

ValueCountFrequency (%)
adult 3958
70.6%
senior 1395
 
24.9%
children 252
 
4.5%

Length

2024-12-03T00:24:49.855585image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T00:24:49.956181image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
adult 3958
70.6%
senior 1395
 
24.9%
children 252
 
4.5%

Most occurring characters

ValueCountFrequency (%)
d 4210
14.0%
l 4210
14.0%
a 3958
13.1%
u 3958
13.1%
t 3958
13.1%
e 1647
 
5.5%
n 1647
 
5.5%
i 1647
 
5.5%
r 1647
 
5.5%
s 1395
 
4.6%
Other values (3) 1899
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 4210
14.0%
l 4210
14.0%
a 3958
13.1%
u 3958
13.1%
t 3958
13.1%
e 1647
 
5.5%
n 1647
 
5.5%
i 1647
 
5.5%
r 1647
 
5.5%
s 1395
 
4.6%
Other values (3) 1899
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 4210
14.0%
l 4210
14.0%
a 3958
13.1%
u 3958
13.1%
t 3958
13.1%
e 1647
 
5.5%
n 1647
 
5.5%
i 1647
 
5.5%
r 1647
 
5.5%
s 1395
 
4.6%
Other values (3) 1899
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 4210
14.0%
l 4210
14.0%
a 3958
13.1%
u 3958
13.1%
t 3958
13.1%
e 1647
 
5.5%
n 1647
 
5.5%
i 1647
 
5.5%
r 1647
 
5.5%
s 1395
 
4.6%
Other values (3) 1899
6.3%

Interactions

2024-12-03T00:24:41.661882image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:34.488432image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:35.993684image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:36.881136image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:37.796715image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:38.865581image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:39.760681image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:40.637704image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:41.827295image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:34.705529image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:36.160955image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:37.061416image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:38.097041image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:39.023280image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:39.918390image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:40.815468image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:41.917533image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:34.877830image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:36.251725image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:37.165504image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:38.211314image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:39.135932image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:40.009681image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:40.918766image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:41.995667image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:35.038267image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:36.340991image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:37.257652image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:38.316972image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:39.245443image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:40.118598image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:41.014714image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:42.084637image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:35.213485image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:36.459274image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:37.364080image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:38.438248image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:39.348190image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:40.226684image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:41.236013image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:42.170928image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:35.378101image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:36.556423image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:37.469722image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:38.531569image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:39.436953image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:40.325643image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:41.338786image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:42.268809image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:35.647272image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:36.662804image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:37.587391image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:38.641703image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:39.547966image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:40.429193image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:41.448913image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:42.421086image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:35.821054image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:36.775131image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:37.697237image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:38.762159image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:39.656343image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:40.536066image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T00:24:41.561630image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2024-12-03T00:24:50.040482image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
age_categoryage_of_patientbase_costcodeethnicitygenderincomeincome_categorylength_of_medication_in_daysmaritalmedicine_dispensespayer_coveragepayer_idracereason_code_for_medicationreason_description_for_medicationtotal_cost_of_medicine
age_category1.0000.9190.0000.6320.1150.1050.2180.2600.0000.5650.1340.0000.4410.3040.4770.4770.000
age_of_patient0.9191.000-0.055-0.1420.2560.3780.3310.6040.0330.524-0.0120.4330.3490.4600.3090.309-0.056
base_cost0.000-0.0551.0000.0790.0720.0210.1860.005-0.0950.0180.1690.4300.0150.0000.0000.0000.925
code0.632-0.1420.0791.0000.4500.553-0.0920.5100.1430.5140.110-0.0930.3620.4730.7640.7640.073
ethnicity0.1150.2560.0720.4501.0000.1180.2540.1540.0370.1880.0590.0720.3460.1360.2330.2330.059
gender0.1050.3780.0210.5530.1181.0000.3200.2720.0100.5090.0390.0060.4890.6890.4960.4960.018
income0.2180.3310.186-0.0920.2540.3201.0000.707-0.0190.2320.0880.1790.4190.2990.2170.2170.197
income_category0.2600.6040.0050.5100.1540.2720.7071.0000.0000.3040.0360.0070.5540.2480.3270.3270.006
length_of_medication_in_days0.0000.033-0.0950.1430.0370.010-0.0190.0001.0000.0000.551-0.1290.0550.0060.3780.3780.051
marital0.5650.5240.0180.5140.1880.5090.2320.3040.0001.0000.0390.0130.4300.3740.4240.4240.027
medicine_dispenses0.134-0.0120.1690.1100.0590.0390.0880.0360.5510.0391.000-0.0040.0950.0640.2350.2350.477
payer_coverage0.0000.4330.430-0.0930.0720.0060.1790.007-0.1290.013-0.0041.0000.0260.0000.0000.0000.376
payer_id0.4410.3490.0150.3620.3460.4890.4190.5540.0550.4300.0950.0261.0000.2880.2350.2350.019
race0.3040.4600.0000.4730.1360.6890.2990.2480.0060.3740.0640.0000.2881.0000.3900.3900.000
reason_code_for_medication0.4770.3090.0000.7640.2330.4960.2170.3270.3780.4240.2350.0000.2350.3901.0001.0000.000
reason_description_for_medication0.4770.3090.0000.7640.2330.4960.2170.3270.3780.4240.2350.0000.2350.3901.0001.0000.000
total_cost_of_medicine0.000-0.0560.9250.0730.0590.0180.1970.0060.0510.0270.4770.3760.0190.0000.0000.0001.000

Missing values

2024-12-03T00:24:42.579358image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-03T00:24:42.908285image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-03T00:24:43.113139image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

start_timestop_timepatient_idpayer_idencounter_idcodemedicine_descriptionbase_costpayer_coveragemedicine_dispensestotal_cost_of_medicinereason_code_for_medicationreason_description_for_medicationlength_of_medication_in_daysmedicine_ended_or_recoveredbirthdatemaritalraceethnicitygenderincomeincome_categoryage_of_patientage_category
02015-09-28 11:02:482015-10-15 09:04:4830a6452c-4297-a1ac-977a-6a23237c7b46d31fccc3-1767-390d-966a-22a5156f4219953c5138-ce17-4084-3432-1ac23f184528857005Acetaminophen 325 MG / HYDROcodone Bitartrate 7.5 MG Oral Tablet2.510.0012.51UnknownUnknown0.04383611994-02-06MwhitenonhispanicM100511high-income21.654795adult
12015-09-28 11:02:482015-10-31 11:02:4830a6452c-4297-a1ac-977a-6a23237c7b46d31fccc3-1767-390d-966a-22a5156f4219953c5138-ce17-4084-3432-1ac23f184528310965Ibuprofen 200 MG Oral Tablet365.900.001365.90UnknownUnknown0.09041111994-02-06MwhitenonhispanicM100511high-income21.654795adult
22005-11-08 20:24:07NaT34a4dcc4-35fb-6ad5-ab98-be285c586a4fe03e23c9-4df1-3eb6-a62d-f70f02301496d1cea2e5-1735-089f-c72f-22ad16976663310325ferrous sulfate 325 MG Oral Tablet0.150.00507.50UnknownUnknownNaN11968-08-06DwhitenonhispanicM49737medium-income37.282192adult
32020-10-30 11:02:482020-11-20 11:02:4830a6452c-4297-a1ac-977a-6a23237c7b46d31fccc3-1767-390d-966a-22a5156f4219794baa15-fe5e-c061-e188-ad59022aeea5313782Acetaminophen 325 MG Oral Tablet153.580.001153.58UnknownUnknown0.05753411994-02-06MwhitenonhispanicM100511high-income26.747945adult
42008-08-27 00:53:032008-08-27 00:53:0334a4dcc4-35fb-6ad5-ab98-be285c586a4f8fa6c185-e44e-3e34-8bd8-39be8694f4ce8a84efee-6fd7-f5b3-8816-9a1c60e720be1535362sodium fluoride 0.0272 MG/MG Oral Gel129.940.001129.9466383009.0Gingivitis (disorder)0.00000011968-08-06DwhitenonhispanicM49737medium-income40.084932adult
52015-09-14 18:32:562015-10-29 18:47:197179458e-d6e3-c723-2530-d4acfe1c2668b046940f-1664-3047-bca7-dfa76be352a4ed8fc369-fd6a-5249-187c-690e5c4524ed313820Acetaminophen 160 MG Chewable Tablet45.860.00145.86UnknownUnknown0.12328812008-12-21UnknownwhitenonhispanicM133816high-income6.734247children
62015-09-28 02:15:462015-09-28 02:15:467179458e-d6e3-c723-2530-d4acfe1c2668b046940f-1664-3047-bca7-dfa76be352a4d3540782-c36d-1535-4760-d2ede629e7f81535362sodium fluoride 0.0272 MG/MG Oral Gel129.940.001129.9481629009.0Traumatic dislocation of temporomandibular joint (disorder)0.00000012008-12-21UnknownwhitenonhispanicM133816high-income6.772603children
72024-02-11 20:40:332024-02-11 20:40:337179458e-d6e3-c723-2530-d4acfe1c2668b046940f-1664-3047-bca7-dfa76be352a4d53a6541-f1cc-03a1-4888-84315485c98a1535362sodium fluoride 0.0272 MG/MG Oral Gel129.940.001129.94103697008.0Patient referral for dental care (procedure)0.00000012008-12-21UnknownwhitenonhispanicM133816high-income15.150685children
82014-06-12 23:25:322015-06-07 23:25:3237c177ea-4398-fb7a-29fa-70eb3d673876df166300-5a78-3502-a46a-832842197811c9a48d36-fbec-2907-6d7e-c3fcb6a5c0de751905Trinessa 28 Day Pack465.41415.41125584.92UnknownUnknown0.98630111994-01-27MasiannonhispanicF17382low-income20.386301adult
92014-11-21 23:25:322014-12-05 23:25:3237c177ea-4398-fb7a-29fa-70eb3d673876df166300-5a78-3502-a46a-83284219781122018eec-bde8-c567-abe0-cc2f6d4e1b8b1049221Acetaminophen 325 MG / Oxycodone Hydrochloride 5 MG Oral Tablet3.970.0013.97UnknownUnknown0.03835611994-01-27MasiannonhispanicF17382low-income20.830137adult
start_timestop_timepatient_idpayer_idencounter_idcodemedicine_descriptionbase_costpayer_coveragemedicine_dispensestotal_cost_of_medicinereason_code_for_medicationreason_description_for_medicationlength_of_medication_in_daysmedicine_ended_or_recoveredbirthdatemaritalraceethnicitygenderincomeincome_categoryage_of_patientage_category
55952019-08-15 09:25:232020-08-20 09:25:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fca735bf55-83e9-331a-899d-a82a60b9f60c0a74b02f-ddd3-06f9-d8e3-adac74fc6ac5308136amLODIPine 2.5 MG Oral Tablet0.910.0043.6459621000.0Essential hypertension (disorder)1.01643811951-11-22SasiannonhispanicF92537high-income67.775342senior
55962020-08-20 09:25:232021-08-26 09:25:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fca735bf55-83e9-331a-899d-a82a60b9f60cce6ff2cc-9b44-3e32-02f6-ebe37611ff97308136amLODIPine 2.5 MG Oral Tablet0.910.0043.6459621000.0Essential hypertension (disorder)1.01643811951-11-22SasiannonhispanicF92537high-income68.791781senior
55972020-09-04 09:25:232020-10-04 09:25:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fca735bf55-83e9-331a-899d-a82a60b9f60c6875cd31-b2d3-6f84-00a2-97d3c3a45266833135Milnacipran hydrochloride 100 MG Oral Tablet154.39123.511154.3995417003.0Primary fibromyalgia syndrome (disorder)0.08219211951-11-22SasiannonhispanicF92537high-income68.832877senior
55982021-05-09 07:25:232021-05-27 07:25:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fca735bf55-83e9-331a-899d-a82a60b9f60c9dd082e9-4cbe-6aa8-081c-df78e86b40da562251Amoxicillin 250 MG / Clavulanate 125 MG Oral Tablet45.8636.69145.86444814009.0Viral sinusitis (disorder)0.04931511951-11-22SasiannonhispanicF92537high-income69.509589senior
55992021-08-26 09:25:232022-09-01 09:25:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fca735bf55-83e9-331a-899d-a82a60b9f60cab2966ff-e387-2938-1bed-93fa0115250f308136amLODIPine 2.5 MG Oral Tablet1.371.1045.4859621000.0Essential hypertension (disorder)1.01643811951-11-22SasiannonhispanicF92537high-income69.808219senior
56002022-09-01 09:25:232023-09-07 09:25:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fca735bf55-83e9-331a-899d-a82a60b9f60c5db88eb9-408d-acaa-779e-e81fd70e81ec308136amLODIPine 2.5 MG Oral Tablet0.910.7343.6459621000.0Essential hypertension (disorder)1.01643811951-11-22SasiannonhispanicF92537high-income70.824658senior
56012022-09-15 14:31:042022-09-15 14:31:04f339a5f7-0b09-3072-2b01-7c8e8ca2c1fca735bf55-83e9-331a-899d-a82a60b9f60c94cc3e70-aa21-8b87-b0c5-f7d12dafdd7b1535362sodium fluoride 0.0272 MG/MG Oral Gel129.94103.951129.9466383009.0Gingivitis (disorder)0.00000011951-11-22SasiannonhispanicF92537high-income70.863014senior
56022023-09-07 09:25:232024-09-12 09:25:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fca735bf55-83e9-331a-899d-a82a60b9f60ce3dfa401-6793-85f8-e430-bffb4d0aec1c308136amLODIPine 2.5 MG Oral Tablet1.370.0045.4859621000.0Essential hypertension (disorder)1.01643811951-11-22SasiannonhispanicF92537high-income71.841096senior
56032024-04-14 09:25:232024-05-14 09:25:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fca735bf55-83e9-331a-899d-a82a60b9f60c03a0d6f1-5131-1f41-35bb-c965b63a3ebd596926duloxetine 20 MG Delayed Release Oral Capsule3.710.0013.7195417003.0Primary fibromyalgia syndrome (disorder)0.08219211951-11-22SasiannonhispanicF92537high-income72.443836senior
56042024-09-12 09:25:23NaTf339a5f7-0b09-3072-2b01-7c8e8ca2c1fca735bf55-83e9-331a-899d-a82a60b9f60c3f31f856-ac64-df94-45b2-9710c15536f8308136amLODIPine 2.5 MG Oral Tablet0.910.0010.9159621000.0Essential hypertension (disorder)NaN11951-11-22SasiannonhispanicF92537high-income72.857534senior